Marketing Program Embraces Big Data Trends

For years, the goal of marketing has been for marketers to gain an understanding of their audience so they can provide products and services to meet customer needs. Now, with the rise of big data, marketers are finding new and better ways to analyze and predict consumer behavior.

"Marketing has become more data driven over the last 30 years, says Sue Fogel of DePaul University's Department of Marketing. "Based on the data about what people have bought, what we can do now is make recommendations for other products that they might like. You can get a very detailed profile of customers and use that to predict what it is they're likely to buy."

The term big data refers to the collection of data that is large and complex and gathered through multiple methods. For marketing, that could be data from point-of-sale scanners at a store, loyalty cards or online shopping habits.

"The data can be very overwhelming — not only the size but also the richness," says Assistant Professor Marina Girju, who teaches undergraduate and graduate classes in which students analyze data. "Most data sets have at least 100 variables that explain consumer behavior, and in my course it's usually over 300. At the beginning, you don't know where to start in terms of looking at it. You can cut it in a million ways and get a million answers."

How DePaul Prepares Marketers

To keep pace with industry trends, DePaul's curriculum includes classes and degrees that prepare marketing students to be comfortable with all forms of data. At the undergraduate level, students can choose from classes on social media marketing, search marketing, Internet marketing, audience segmentation and targeting, and more.

At the graduate level, DePaul offers an MS in Marketing Analysis, which grew from database marketing into predictive modeling. Fogel notes that when the master's degree began, it was enrolling about 12 students annually. Now there are around 50 new students each year, including a sizeable group of international students.

The Department of Marketing also collaborates with DePaul's College of Computing and Digital Media. Marketing faculty teach marketing analytics courses as part of CDM's MS in Predictive Analytics and sit on the faculty board of the Center for Data Mining & Predictive Analytics (DAMPA). The MSPA degree also features a health care analysis concentration that teaches students to apply big data to the health care field.

No matter the class, students learn through DePaul's model of combining the theoretical with the practical. "We have a hands-on approach: For every model that I'm introducing, I also have a project where the students have to apply the model to the data set and put everything in the perspective of the consumer or business so we can understand how the analysis is going to influence all of the parties involved," says Girju. "A piece of analysis is important, but it is so much better if it's actually put in context of trends and challenges in the industry."

Girju knows about putting data into context. Before joining DePaul in 2012, she worked for TNS, a leader in consumer market research, as a data analyst. Her job for the company's client, Frito-Lay, was to study the data generated by thousands of U.S. consumers who recorded details about their snacking habits. In total, more than 300 variables were tracked over several years. From that, Girju and her co-researchers developed DemoImpact, a forecasting model for predicting snack consumption for hundreds of snacks in dozens of categories.

One of Girju's newer classes also focuses on forecasting how people will act. In spring 2014, she taught Predicting Choice Behavior, which drew a mix of 25 master- and doctorate-level students. (Newly launched classes are often considered successful if seven to 10 students sign up.)

"There are two big challenges for students: one that comes at the very beginning — how do I approach analyzing so much data? — and one at the end — I have all the results but I have to put them together in a single story, a story that points to clear insights that a company can act on," Girju explains. "The middle, the analysis part, is pretty straightforward once we cover a large set of marketing and statistical models in class."

Because of the class's popularity, it now will be offered each year in the spring. A similar course, MKT 798 Health Care Data Analysis, focusing on the health care industry, is being taught by Assistant Professor Andrew Gallan. In addition, several students from Girju's spring course have shown interest in publishing their class projects. She is working with them beyond the class — as some students have already graduated — to write up their analyses to submit for the SAS Global Forum Conference, a large analytics conference where business analysts publish their approaches to solving a variety of business problems.

How Data Shapes the Industry

Employment opportunities are rising for people with marketing and analytics backgrounds, and students who have worked with data during college will be better prepared to find analytical jobs after graduation. "It's a huge growth field. The most common role is not so much absolute number crunching but being in a position where you can take the results of an analysis and explain what it means," Fogel says. "Somebody who's trained in it can make recommendations about what you can do based on the results."

Before there was a focus on data analysis, Fogel notes that researchers had to rely on "the golden gut" — making educated guesses about what did and didn't work with customers because measurement wasn't as accurate. Marketers could field an attitude survey about a product but might get back limited insights. Today's technology makes it easier, Fogel says: "Now you can say, 'We're going to target this message to this person and we will know for sure whether they bought the product and when they bought it.'"

Today, data can also show which customers are worth keeping. "You've heard 'the customer is always right,' but we teach that that's not necessarily true. Some customers demand a lot of resources and don't necessarily make a lot of purchases," Fogel explains. Through an analysis of consumer transactions, businesses can determine someone's customer lifetime value.

"Some people will be highly valuable to you, so you should spend a lot of resources in keeping them because they will continue to give you a stream of profits," she says. "There are others who are always on the customer service line, always returning products or wanting extra service. Those are the people you might as well steer to your competitors."